{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 使用Keras來破解captcha驗證碼"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "當人們在網站註冊或購物時，經常會出現圖像驗證碼的輸入要求，為了區分「真人」，圖片會出現線條及扭曲分隔開的文字，讓 爬蟲/惡意程式難以辨識，但這種方式因為深度學習的視覺辨識能力的增強而容易被破解。\n",
    "\n",
    "本文會通過Keras搭建一個深度卷積神經網絡來識別一個固定長度captcha驗證碼。\n",
    "\n",
    "**captcha**是用python寫的生成驗證碼的函式庫，它支持動態產生圖片驗證碼和語音驗證碼，我們使用的是它生成圖片驗證碼的功能來產生模型的訓練資料與驗證資料。並透過Keras的generator功能來直接使用captcha所產生的圖像, 而不需要預先把資料產生在檔案中。\n",
    "\n",
    "![captcha](https://geekreply.com/wp-content/uploads/2017/10/captcha-136419892493002601.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Captcha驗證碼\n",
    "首先我們設置我們的驗證碼格式為數字加大寫字母，生成一串驗證碼試試看："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ\n"
     ]
    },
    {
     "data": {
      "image/png": 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lmVVUm81wjLVKMSGXVwJ4k4i8AUAbgB6oYu8VkSZT6XsBnCvfMB3HcZzVWPWC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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x169fe9a89e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 載入產生Capthca的函式庫\n",
    "from captcha.image import ImageCaptcha\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import random\n",
    "\n",
    "import string\n",
    "\n",
    "characters = string.digits + string.ascii_uppercase\n",
    "print(characters) # 打印一下會用到的字母\n",
    "\n",
    "# Captcha生成圖像的寬高、用幾個字母來組成字串及會用到的字母數\n",
    "width, height, n_len, n_class = 170, 80, 4, len(characters)\n",
    "\n",
    "generator = ImageCaptcha(width=width, height=height) # 設定captcha圖像的長寬\n",
    "\n",
    "# 產生4個字母的captcha\n",
    "random_str = ''.join([random.choice(characters) for j in range(4)]) \n",
    "img = generator.generate_image(random_str)\n",
    "\n",
    "# 把Captcha秀出來\n",
    "plt.imshow(img)\n",
    "plt.title(random_str)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 數據生成器"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "訓練模型的時候，我們可以選擇兩種方式來生成我們的訓練數據，一種是一次性生成幾萬張圖，\n",
    "然後開始訓練，一種是定義一個數據生成器，然後利用fit_generator函數來訓練。\n",
    "\n",
    "第一種方式的好處是訓練的時候顯卡利用率高，如果你需要經常調參，可以一次生成，多次使用;\n",
    "第二種方式的好處是你不需要生成大量數據，訓練過程中可以利用CPU生成數據，而且還有一個好處\n",
    "是你可以無限生成數據。\n",
    "\n",
    "我們的數據格式如下：\n",
    "\n",
    "### X\n",
    "\n",
    "X的形狀是（batch_size，height，width，3），比如生成一個有32個彩色樣本的批次，圖片高度為80，寬度為170，那麼張量形狀就是（32，80，170，3），取第一張圖就是X[0]。\n",
    "\n",
    "### y\n",
    "\n",
    "y的張量形狀是四個（batch_size，n_class），如果轉換成numpy的格式，則是（n_len，batch_size，n_class），比如一批生成32個樣本，驗證碼的字符有36種，長度是4位， 那麼它的形狀就是4個（32，36），也可以說是（4，32，36）。\n",
    "\n",
    "ps.請特別注意**y**的資料結構(4, 32, 36)是會對應到網絡4個softmax的輸出"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def gen(batch_size=32):\n",
    "    X = np.zeros((batch_size, height, width, 3), dtype=np.uint8)\n",
    "    y = [np.zeros((batch_size, n_class), dtype=np.uint8) for i in range(n_len)]\n",
    "    generator = ImageCaptcha(width=width, height=height)\n",
    "    while True:\n",
    "        for i in range(batch_size):\n",
    "            random_str = ''.join([random.choice(characters) for j in range(4)])\n",
    "            X[i] = generator.generate_image(random_str)\n",
    "            for j, ch in enumerate(random_str):\n",
    "                y[j][i, :] = 0\n",
    "                y[j][i, characters.find(ch)] = 1\n",
    "        yield X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面就是一個可以無限生成圖像數據的例子，我們將使用這個生成器來產生圖像資料來訓練我們的模型。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用生成器\n",
    "\n",
    "生成器的使用方法很簡單，只需要使用next()函數即可。下面是一個範例，生成32個圖像數據，然後顯示第一個圖像數據。當然，在這裡我們還對生成的One-Hot編碼後的數據進行解碼，首先將它轉為numpy數組，然後取36個字符中最大的數字的位置，因為神經網絡會輸出36個字符的機率，然後將機率最大的四個字符的編號轉換為字串。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RbJtyWZTe2Qvym3lTfc+na9KiuD4sbn4xIeo2JCVXfCauLEvzNTFP5/7euV3i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\nf9BCaQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x169fc076cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def decode(y):\n",
    "    y = np.argmax(np.array(y), axis=2)[:,0]\n",
    "    return ''.join([characters[x] for x in y])\n",
    "\n",
    "X, y = next(gen(1)) # 產生一個批次數量為1的數據\n",
    "\n",
    "plt.imshow(X[0])\n",
    "plt.title(decode(y))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 構建深度卷積神經網絡"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "模型結構很簡單，特徵提取部分使用的是兩個卷積，一個池化的結構，這個結構是學VGG16的結構。之後我們將它展開，然後添加Dropout，\n",
    "盡量避免過擬合(overfitting)問題，最後連接四個分類器，每個分類器是36個神經元，輸出36個字符的機率。\n",
    "這個網絡模型比較特別的地方就在最後的四個分類器的設計, 有關\"多輸出模型\"的說明可以參考:[如何使用Keras函數式API進行深度學習](https://github.com/erhwenkuo/deep-learning-with-keras-notebooks/blob/master/1.1-keras-functional-api.ipynb) - [5.2 多輸出模型]。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 80, 170, 3)   0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 78, 168, 32)  896         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 76, 166, 32)  9248        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 38, 83, 32)   0           conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 36, 81, 64)   18496       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 34, 79, 64)   36928       conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 17, 39, 64)   0           conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 15, 37, 128)  73856       max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 13, 35, 128)  147584      conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 6, 17, 128)   0           conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 4, 15, 256)   295168      max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 2, 13, 256)   590080      conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2D)  (None, 1, 6, 256)    0           conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 1536)         0           max_pooling2d_4[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 1536)         0           flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "c1 (Dense)                      (None, 36)           55332       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "c2 (Dense)                      (None, 36)           55332       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "c3 (Dense)                      (None, 36)           55332       dropout_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "c4 (Dense)                      (None, 36)           55332       dropout_1[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 1,393,584\n",
      "Trainable params: 1,393,584\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "from keras.models import *\n",
    "from keras.layers import *\n",
    "\n",
    "input_tensor = Input((height, width, 3))\n",
    "x = input_tensor\n",
    "\n",
    "# 產生有四個 block的卷積網絡\n",
    "for i in range(4):\n",
    "    x = Conv2D(32*2**i, (3, 3), activation='relu')(x)\n",
    "    x = Conv2D(32*2**i, (3, 3), activation='relu')(x)\n",
    "    x = MaxPooling2D((2, 2))(x)\n",
    "    \n",
    "x = Flatten()(x)\n",
    "x = Dropout(0.25)(x)\n",
    "\n",
    "# 多輸出模型, 由次這個captcha是有4個字母(固定長度), 因此我們對應使用了4個'softmax'來分別預測4個字母的產出\n",
    "x = [Dense(n_class, activation='softmax', name='c%d'%(i+1))(x) for i in range(4)]\n",
    "\n",
    "model = Model(inputs=input_tensor, outputs=x)\n",
    "\n",
    "model.summary()\n",
    "\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adadelta',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型可視化\n",
    "\n",
    "得益於Keras自帶的可視化工具，我們可以使用幾行程式碼來可視化模型的結構：\n",
    "\n",
    "Ps.這裡需要使用pydot以及graphviz這兩個函式庫。使用Windows作業系統的人可以參考-[[Setting_up_pydot_for_Python_3_5_Keras_Conda_Windows_64](http://justinwatson.name/2017/02/10/Setting_up_pydot_for_Python_3_5_Keras_Conda_Windows_64.html)]來進行設定安裝"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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c3Gzrn/VkMqnXX39dly5dkmVZLWlzZmZG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\ndxqpZVll0xPr6XPpXrgWDod1/Phx2batZ599VkNDQ3XVsBsymYw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/Gv//qvuOmmm/DSSy/BYDCoHRJRSLxXFG9vb8eJEydGrSiek5ODO++8k1NMEBFFCQtsREQR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\nTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "from IPython.display import Image\n",
    "\n",
    "plot_model(model, to_file=\"model.png\", show_shapes=True)\n",
    "Image('model.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我們可以看到最後一層卷積層輸出的張量形狀是（1，6，256），已經不能再加卷積層了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 訓練模型\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "訓練模型反而是所有步驟裡面最簡單的一個，直接使用model.fit_generator即可，這裡的驗證集使用了同樣的生成器，\n",
    "由於數據是通過生成器隨機生成的，所以我們不用考慮數據是否會重複。\n",
    "\n",
    "注意，這段程式碼可能要耗費一下的時間。如果你想讓模型預測得更準確，可以將nb_epoch改為10或者20，但它也會耗費成倍的時間。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "1600/1600 [==============================] - 99s 62ms/step - loss: 14.3638 - c1_loss: 3.5906 - c2_loss: 3.5903 - c3_loss: 3.5907 - c4_loss: 3.5923 - c1_acc: 0.0285 - c2_acc: 0.0283 - c3_acc: 0.0292 - c4_acc: 0.0286 - val_loss: 14.3333 - val_c1_loss: 3.5834 - val_c2_loss: 3.5824 - val_c3_loss: 3.5834 - val_c4_loss: 3.5841 - val_c1_acc: 0.0250 - val_c2_acc: 0.0305 - val_c3_acc: 0.0312 - val_c4_acc: 0.0352\n",
      "Epoch 2/5\n",
      "1600/1600 [==============================] - 97s 60ms/step - loss: 6.3264 - c1_loss: 1.9295 - c2_loss: 1.3399 - c3_loss: 1.4713 - c4_loss: 1.5857 - c1_acc: 0.4729 - c2_acc: 0.5997 - c3_acc: 0.5716 - c4_acc: 0.5768 - val_loss: 1.3688 - val_c1_loss: 0.4813 - val_c2_loss: 0.1518 - val_c3_loss: 0.2378 - val_c4_loss: 0.4979 - val_c1_acc: 0.9117 - val_c2_acc: 0.9594 - val_c3_acc: 0.9313 - val_c4_acc: 0.9125\n",
      "Epoch 3/5\n",
      "1600/1600 [==============================] - 97s 61ms/step - loss: 1.3388 - c1_loss: 0.5041 - c2_loss: 0.1258 - c3_loss: 0.2028 - c4_loss: 0.5060 - c1_acc: 0.9111 - c2_acc: 0.9609 - c3_acc: 0.9366 - c4_acc: 0.9161 - val_loss: 0.9151 - val_c1_loss: 0.3654 - val_c2_loss: 0.0608 - val_c3_loss: 0.0972 - val_c4_loss: 0.3917 - val_c1_acc: 0.9500 - val_c2_acc: 0.9781 - val_c3_acc: 0.9695 - val_c4_acc: 0.9484\n",
      "Epoch 4/5\n",
      "1600/1600 [==============================] - 97s 61ms/step - loss: 0.9903 - c1_loss: 0.3935 - c2_loss: 0.0698 - c3_loss: 0.1133 - c4_loss: 0.4137 - c1_acc: 0.9453 - c2_acc: 0.9778 - c3_acc: 0.9654 - c4_acc: 0.9424 - val_loss: 0.8008 - val_c1_loss: 0.3048 - val_c2_loss: 0.0550 - val_c3_loss: 0.0954 - val_c4_loss: 0.3456 - val_c1_acc: 0.9570 - val_c2_acc: 0.9852 - val_c3_acc: 0.9648 - val_c4_acc: 0.9516\n",
      "Epoch 5/5\n",
      "1600/1600 [==============================] - 97s 61ms/step - loss: 0.8766 - c1_loss: 0.3586 - c2_loss: 0.0554 - c3_loss: 0.0872 - c4_loss: 0.3753 - c1_acc: 0.9542 - c2_acc: 0.9826 - c3_acc: 0.9732 - c4_acc: 0.9496 - val_loss: 0.7666 - val_c1_loss: 0.3492 - val_c2_loss: 0.0319 - val_c3_loss: 0.0607 - val_c4_loss: 0.3247 - val_c1_acc: 0.9602 - val_c2_acc: 0.9906 - val_c3_acc: 0.9781 - val_c4_acc: 0.9547\n"
     ]
    }
   ],
   "source": [
    "from keras import callbacks\n",
    "cbks = [callbacks.ModelCheckpoint(\"best_model.h5\", save_best_only=True)]\n",
    "\n",
    "history = model.fit_generator(gen(batch_size=32),      # 每次生成器會產生32筆小批量的資料\n",
    "                    steps_per_epoch=1600,    # 每次的epoch要訓練1600批量的資料\n",
    "                    epochs=5,                # 總共跑5個訓練循環\n",
    "                    callbacks=cbks,          # 保存最好的模型到檔案\n",
    "                    validation_data=gen(),   # 驗證資料也是用生成器來產生\n",
    "                    validation_steps=40      # 用40組資料來驗證\n",
    "                   ) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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k6HcxLH8VKqy5nTOtSzyzbxyC3QZXzFxE3nbt0lEqlFh119DvgFkikgcMAv5u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\nd6JlsQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16bedeb5390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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uv+02PTufF7/byaw/DrkeL96DgZ2aoJSiQa3wwtE/5JG3QogCNf7soj3PuDEx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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16bedeb5320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 透過趨勢圖來觀察訓練與驗證的走向 (特別去觀察是否有\"過擬合(overfitting)\"的現象)\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_train_history(history, train_metrics, val_metrics):\n",
    "    plt.plot(history.history.get(train_metrics),'-o')\n",
    "    plt.plot(history.history.get(val_metrics),'-o')\n",
    "    plt.ylabel(train_metrics)\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.legend(['train', 'validation'])\n",
    "\n",
    "# 打印整體的loss與val_loss\n",
    "plot_train_history(history, 'loss', 'val_loss')\n",
    "    \n",
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "# 第一個字母的正確率\n",
    "plt.subplot(2,2,1)\n",
    "plot_train_history(history, 'c1_acc','val_c1_acc')\n",
    "\n",
    "# 第二個字母的正確率\n",
    "plt.subplot(2,2,2)\n",
    "plot_train_history(history, 'c2_acc','val_c2_acc')\n",
    "\n",
    "# 第三個字母的正確率\n",
    "plt.subplot(2,2,3)\n",
    "plot_train_history(history, 'c3_acc','val_c3_acc')\n",
    "\n",
    "# 第四個字母的正確率\n",
    "plt.subplot(2,2,4)\n",
    "plot_train_history(history, 'c4_acc','val_c4_acc')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 測試模型\n",
    "\n",
    "當我們訓練完成以後，可以識別一個驗證碼試試看："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from keras.models import load_model\n",
    "\n",
    "del model  # 移除現在在記憶體的模型\n",
    "model = load_model('best_model.h5') # 載入在訓練過程儲存下來的模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A4eOsUjeuW7Wg49QCrbrjJM8/OAs3p8TV5VC2V2Uv26ufiVYwGpWlKvREYnGz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\n+OkpeW+aI0SBZXgol/IOElT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6yJckolYAXwfwXufceK3bAwBEdCeAAefck/lPF9i0Fn0Y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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x16bed5bda58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X, y = next(gen(1)) # 產生一筆新的測試資料\n",
    "y_pred = model.predict(X) # 進行預測\n",
    "\n",
    "# 展示結果\n",
    "plt.title('real: %s\\npred:%s'%(decode(y), decode(y_pred)))\n",
    "plt.imshow(X[0], cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 計算模型總體準確率\n",
    "\n",
    "模型在訓練的時候只會顯示每一個字符的準確率，為了統計模型的總體準確率，我們可以寫下面的函數："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████████████████████████████████████████████| 20/20 [00:01<00:00, 15.06it/s]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.9390625"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "def evaluate(model, batch_num=20):\n",
    "    batch_acc = 0 # 對的筆數\n",
    "    batch_count = 0 # 總筆數\n",
    "    \n",
    "    generator = gen()\n",
    "    for i in tqdm(range(batch_num)):\n",
    "        X, y = next(generator) # 產生一批次的圖像資料與答案\n",
    "        y_pred = model.predict(X) # 進行預測\n",
    "        y_pred = np.argmax(y_pred, axis=2).T # 轉置矩陣 (4, 32) -> (32, 4)\n",
    "        y_true = np.argmax(y, axis=2).T # 轉置矩陣 (4, 32) -> (32, 4)\n",
    "        \n",
    "        # 比對兩個矩陣是每個cell(character)都是相同的, 全部相同才算答對\n",
    "        for i in range(len(y_pred)):\n",
    "            batch_count += 1\n",
    "            if np.array_equal(y_true[i], y_pred[i]):\n",
    "                batch_acc += 1\n",
    "    # 計算正確率\n",
    "    return batch_acc / batch_count\n",
    "\n",
    "# 讓我們測試一下模型的總體準確率\n",
    "evaluate(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "這裡用到了一個函式庫叫做tqdm，它是一個展示進度條的函式庫，為的是能夠實時反饋程式執行的進度。\n",
    "\n",
    "我們透過一些numpy計算去統計我們的預測準確率，這裡計算規則是四個產生值裡只要有一個錯，那麼就不算它對。經過計算，我們的模型的總體準確率在經過5次的循環後就可以達到91%左右，繼續訓練還可以達到更高的準確率。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型總結\n",
    "\n",
    "模型的大小是16MB，在我的筆記本上跑1000張驗證碼需要用20秒，當然，顯卡會更快。對於驗證碼識別的問題來說，哪怕是10％的準確率也已經稱得上破解，畢竟假設100％識別率破解要一個小時，那麼10％的識別率也只用十個小時，還算等得起，而我們的識別率有91％，已經可以稱得上破解了這類驗證碼的安全性了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 改進\n",
    "\n",
    "以上模型對於固定長度的captcha己經可以有了很不錯的辨識結果, 但是對於不定長度的captcha就無法應付了。如果要繼續改進不定長度的對captcha，我們可以使用遞歸神經網絡(RNN)來識別文字序列。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 總結 (Conclusion)\n",
    "\n",
    "在這篇文章中有一些個人學習到的一些有趣的重點:\n",
    "    \n",
    "* 一個由卷積網絡所淬取出的features map如何被多個output所使用\n",
    "* 需要了解網絡的結構與不同網絡層輸入輸出的張量的結構才能夠清楚地構建一個對的模型\n",
    "* 深度學習在圖像的應用真的需要想像力、創造力與實作力"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "參考: \n",
    "* [使用 Keras 来破解 captcha 验证码](https://ypw.io/captcha/)\n",
    "* [Keras範例-image_orc.py](https://github.com/fchollet/keras/blob/master/examples/image_ocr.py)\n",
    "* [Keras官網](http://keras.io/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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